Oikonomou Evangelos K, Sangha Veer, Vasisht Shankar Sumukh, Coppi Andreas, Krumholz Harlan M, Nasir Khurram, Miller Edward J, Gallegos Kattan Cesia, Al-Mallah Mouaz H, Al-Kindi Sadeer, Khera Rohan
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA.
Eur Heart J. 2025 Jul 18. doi: 10.1093/eurheartj/ehaf450.
The diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale preclinical testing. Artificial intelligence (AI)-enabled transthoracic echocardiography (TTE) and electrocardiography (ECG) may provide a scalable strategy for preclinical monitoring.
This was a retrospective analysis of individuals referred for nuclear cardiac amyloid testing at the Yale-New Haven Health System (YNHHS, internal cohort) and Houston Methodist Hospitals (HMH, external cohort). Deep learning models trained to discriminate ATTR-CM from age/sex-matched controls on TTE videos (AI-Echo) and ECG images (AI-ECG) were deployed to generate study-level ATTR-CM probabilities (0%-100%). Longitudinal trends in AI-derived probabilities were examined using age/sex-adjusted linear mixed models, and their discrimination of future disease was evaluated across preclinical stages.
Among 984 participants at YNHHS (median age 74 years, 44.3% female) and 806 at HMH (median age 69 years, 34.5% female), 112 (11.4%) and 174 (21.6%) tested positive for ATTR-CM, respectively. Across cohorts and modalities, AI-derived ATTR-CM probabilities from 7352 TTEs and 32 205 ECGs diverged as early as 3 years before diagnosis in cases vs controls (ptime(x)group interaction ≤ .004). Among those with both AI-Echo and AI-ECG probabilities available 1 to 3 years before nuclear testing [n = 433 (YNHHS) sand 174 (HMH)], a double-negative screen at a 0.05 threshold [164 (37.9%) and 66 (37.9%), vs all else] had 90.9% and 85.7% sensitivity (specificity of 40.3% and 41.2%), whereas a double-positive screen [78 (18.0%) and 26 (14.9%), vs all else] had 85.5% and 88.9% specificity (sensitivity of 60.6% and 42.9%).
Artificial intelligence-enabled echocardiography and electrocardiography may enable scalable risk stratification of ATTR-CM during its preclinical course.
转甲状腺素蛋白淀粉样变心肌病（ATTR-CM）的诊断需要先进的影像学检查，这使得大规模的临床前检测难以实现。基于人工智能（AI）的经胸超声心动图（TTE）和心电图（ECG）可能为临床前监测提供一种可扩展的策略。
这是一项对在耶鲁-纽黑文医疗系统（YNHHS，内部队列）和休斯顿卫理公会医院（HMH，外部队列）接受核心脏淀粉样变检测的个体进行的回顾性分析。训练用于在TTE视频（AI-Echo）和ECG图像（AI-ECG）上区分ATTR-CM与年龄/性别匹配对照的深度学习模型被用于生成研究水平的ATTR-CM概率（0%-100%）。使用年龄/性别调整的线性混合模型检查AI衍生概率的纵向趋势，并在临床前阶段评估它们对未来疾病的鉴别能力。
在YNHHS的984名参与者（中位年龄74岁，44.3%为女性）和HMH的806名参与者（中位年龄69岁，34.5%为女性）中，分别有112名（11.4%）和174名（21.6%）ATTR-CM检测呈阳性。在各个队列和检查方式中，来自7352次TTE和3万2205份ECG的AI衍生ATTR-CM概率在病例组与对照组中早在诊断前3年就出现了差异（时间（x）×组交互作用≤0.004）。在核检测前1至3年同时有AI-Echo和AI-ECG概率的人群中[n = 433（YNHHS）和174（HMH）]，0.05阈值下的双阴性筛查[164名（37.9%）和66名（37.9%），与其他所有情况相比]的灵敏度分别为90.9%和85.7%（特异性为40.3%和41.2%），而双阳性筛查[78名（18.0%）和26名（14.9%），与其他所有情况相比]的特异性分别为85.5%和88.9%（灵敏度为60.6%和42.9%）。
基于人工智能的超声心动图和心电图可能在ATTR-CM的临床前病程中实现可扩展的风险分层。
